research-article

Saliency detection for RGBD images

Authors:

Cong BaiAuthors Info & Claims

ICIMCS '15: Proceedings of the 7th International Conference on Internet Multimedia Computing and Service

Article No.: 72, Pages 1 - 4

https://doi.org/10.1145/2808492.2808565

Published: 19 August 2015 Publication History

Abstract

Additional depth information from RGBD images is one of characteristics different from conventional 2D images. In this paper, we propose an effective saliency model to detect salient regions in RGBD images. Color contrast and depth contrast are first enhanced with the weighting of depth-based object probability. Then the region merging based saliency refinement is exploited to obtain the color saliency map and depth saliency map, respectively. Finally, a location prior of salient objects is integrated with color saliency and depth saliency to obtain the regional saliency map. Both subjective and objective evaluations on a public RGBD image dataset demonstrate that the proposed saliency model outperforms the state-of-the-art saliency models.

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Cited By

Ji WYan GLi JPiao YYao SZhang MCheng LLu H(2022)DMRA: Depth-Induced Multi-Scale Recurrent Attention Network for RGB-D Saliency DetectionIEEE Transactions on Image Processing10.1109/TIP.2022.315493131(2321-2336)Online publication date: 2022
https://doi.org/10.1109/TIP.2022.3154931
Han YWang LDu AJiang S(2022)LIANet: Layer Interactive Attention Network for RGB-D Salient Object DetectionIEEE Access10.1109/ACCESS.2022.315693510(25435-25447)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3156935
Li GLiu ZChen MBai ZLin WLing H(2021)Hierarchical Alternate Interaction Network for RGB-D Salient Object DetectionIEEE Transactions on Image Processing10.1109/TIP.2021.306268930(3528-3542)Online publication date: 2021
https://doi.org/10.1109/TIP.2021.3062689
Show More Cited By

Index Terms

Saliency detection for RGBD images
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Scene understanding

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Published In

cover image ACM Other conferences

ICIMCS '15: Proceedings of the 7th International Conference on Internet Multimedia Computing and Service

August 2015

397 pages

ISBN:9781450335287

DOI:10.1145/2808492

General Chairs:
Ramesh Jain
University of California, Irvine
,
Shuqiang Jiang
Institute of Computing Technology, Chinese Academy of Sciences, China
,
Program Chairs:
John Smith
IBM Thomas J. Watson Research Center
,
Jitao Sang
Institute of Automation, Chinese Academy of Sciences, China
,
Guohui Li
National University of Defense Technology, China

Copyright © 2015 ACM.

© 2015 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 August 2015

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Funding Sources

National Natural Science Foundation of China
Shanghai Institutions of Higher Learning

Conference

ICIMCS '15

ICIMCS '15: International Conference on Internet Multimedia Computing and Service

August 19 - 21, 2015

Hunan, Zhangjiajie, China

Acceptance Rates

ICIMCS '15 Paper Acceptance Rate 20 of 128 submissions, 16%;

Overall Acceptance Rate 163 of 456 submissions, 36%

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Citations

Cited By

Ji WYan GLi JPiao YYao SZhang MCheng LLu H(2022)DMRA: Depth-Induced Multi-Scale Recurrent Attention Network for RGB-D Saliency DetectionIEEE Transactions on Image Processing10.1109/TIP.2022.315493131(2321-2336)Online publication date: 2022
https://doi.org/10.1109/TIP.2022.3154931
Han YWang LDu AJiang S(2022)LIANet: Layer Interactive Attention Network for RGB-D Salient Object DetectionIEEE Access10.1109/ACCESS.2022.315693510(25435-25447)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3156935
Li GLiu ZChen MBai ZLin WLing H(2021)Hierarchical Alternate Interaction Network for RGB-D Salient Object DetectionIEEE Transactions on Image Processing10.1109/TIP.2021.306268930(3528-3542)Online publication date: 2021
https://doi.org/10.1109/TIP.2021.3062689
Li GLiu ZYe LWang YLing H(2020)Cross-Modal Weighting Network for RGB-D Salient Object DetectionComputer Vision – ECCV 202010.1007/978-3-030-58520-4_39(665-681)Online publication date: 19-Nov-2020
https://doi.org/10.1007/978-3-030-58520-4_39
Liu ZSong TXie F(2019)RGB-D image saliency detection from 3D perspectiveMultimedia Tools and Applications10.1007/s11042-018-6319-478:6(6787-6804)Online publication date: 17-May-2019
https://dl.acm.org/doi/10.1007/s11042-018-6319-4
Lei ZChai WZhao SSong HLi F(2017)Saliency detection for RGBD image using optimization2017 12th International Conference on Computer Science and Education (ICCSE)10.1109/ICCSE.2017.8085532(440-443)Online publication date: Aug-2017
https://doi.org/10.1109/ICCSE.2017.8085532
Song HLiu ZDu HSun G(2016)Depth-aware saliency detection using discriminative saliency fusion2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP.2016.7471952(1626-1630)Online publication date: Mar-2016
https://doi.org/10.1109/ICASSP.2016.7471952
Du HLiu ZSong HMei LXu Z(2016)Improving RGBD Saliency Detection Using Progressive Region Classification and Saliency FusionIEEE Access10.1109/ACCESS.2016.26327244(8987-8994)Online publication date: 2016
https://doi.org/10.1109/ACCESS.2016.2632724

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